基于深度网络的垃圾识别与分类算法研究源码（高分毕设）.zip

# 导入torch 相关包 import time import torch import torch.nn as nn # 导入模型定义方法 import models # 导入工具类 from utils.eval import accuracy from utils.misc import AverageMeter import numpy as np # 导入进度条库 from progress.bar import Bar #from progressbar import Bar device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') def train(train_loader, model, criterion, optimizer): ''' 模型训练 :param train_loader: :param model: :param criterion: :param optimizer: :return: ''' # 定义保存更新变量 data_time = AverageMeter() batch_time = AverageMeter() losses = AverageMeter() top1 = AverageMeter() end = time.time() ################# # train the model ################# model.train() # 训练每批数据，然后进行模型的训练 ## 定义bar 变量 bar = Bar('Processing',max = len(train_loader)) for batch_index, (inputs, targets) in enumerate(train_loader): data_time.update(time.time() - end) # move tensors to GPU if cuda is_available inputs, targets = inputs.to(device), targets.to(device) # 在进行反向传播之前，我们使用zero_grad方法清空梯度 optimizer.zero_grad() # 模型的预测 outputs = model(inputs) # 计算loss loss = criterion(outputs, targets) # backward pass: loss.backward() # perform as single optimization step (parameter update) optimizer.step() # 计算acc和变量更新 prec1, _ = accuracy(outputs.data, targets.data, topk=(1, 1)) losses.update(loss.item(), inputs.size(0)) top1.update(prec1.item(), inputs.size(0)) batch_time.update(time.time() - end) end = time.time() # plot progress ## 把主要的参数打包放进bar中 # plot progress bar.suffix = '({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | Total: {total:} | ETA: {eta:} | Loss: {loss:.4f} | top1: {top1: .4f}'.format( batch=batch_index + 1, size=len(train_loader), data=data_time.val, bt=batch_time.val, total=bar.elapsed_td, eta=bar.eta_td, loss=losses.avg, top1=top1.avg ) bar.next() bar.finish() return (losses.avg, top1.avg) def evaluate(val_loader,model, criterion,test = None): ''' 模型评估 :param val_loader: :param model: :param criterion: :param test: :return: ''' global best_acc batch_time = AverageMeter() data_time = AverageMeter() losses = AverageMeter() top1 = AverageMeter() predict_all = np.array([],dtype=int) labels_all = np.array([],dtype=int) ################# # val the model ################# model.eval() end = time.time() # 训练每批数据，然后进行模型的训练 ## 定义bar 变量 bar = Bar('Processing', max=len(val_loader)) for batch_index, (inputs, targets) in enumerate(val_loader): data_time.update(time.time() - end) # move tensors to GPU if cuda is_available inputs, targets = inputs.to(device), targets.to(device) # 模型的预测 outputs = model(inputs) # 计算loss loss = criterion(outputs, targets) # 计算acc和变量更新 prec1, _ = accuracy(outputs.data, targets.data, topk=(1, 1)) losses.update(loss.item(), inputs.size(0)) top1.update(prec1.item(), inputs.size(0)) batch_time.update(time.time() - end) end = time.time() # 评估混淆矩阵的数据 targets = targets.data.cpu().numpy() # 真实数据的y数值 predic = torch.max(outputs.data,1)[1].cpu().numpy() # 预测数据y数值 labels_all = np.append(labels_all,targets) # 数据赋值 predict_all = np.append(predict_all,predic) ## 把主要的参数打包放进bar中 # plot progress bar.suffix = '({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | Total: {total:} | ETA: {eta:} | Loss: {loss:.4f} | top1: {top1: .4f}'.format( batch=batch_index + 1, size=len(val_loader), data=data_time.val, bt=batch_time.val, total=bar.elapsed_td, eta=bar.eta_td, loss=losses.avg, top1=top1.avg ) bar.next() bar.finish() if test: return (losses.avg, top1.avg,predict_all,labels_all) else: return (losses.avg, top1.avg) def set_parameter_requires_grad(model, feature_extract): ''' :param model: 模型 :param feature_extract: true 固定特征抽取层 :return: ''' if feature_extract: for param in model.parameters(): # 不需要更新梯度，冻结某些层的梯度 param.requires_grad = False def initital_model(model_name, num_classes, feature_extract=True): """ 基于提供的pre_trained_model 进行初始化 :param model_name: 提供的模型名称，例如： resnext101_32x16d/resnext101_32x8d.. :param num_classes: 图片分类个数 :param feature_extract: 设置true ，固定特征提取层，优化全连接的分类器 :return: """ model_ft = None if model_name == 'resnext101_32x16d': # 加载facebook pre_trained_model resnext101,默认1000 类 model_ft = models.resnext101_32x16d_wsl() # 设置 固定特征提取层 set_parameter_requires_grad(model_ft, feature_extract) # 调整分类个数 num_ftrs = model_ft.fc.in_features # 修改fc 的分类个数 model_ft.fc = nn.Sequential( nn.Dropout(0.2), # 防止过拟合 nn.Linear(in_features=num_ftrs, out_features=num_classes) ) elif model_name == 'resnext101_32x8d': # 加载facebook pre_trained_model resnext101,默认1000 类 model_ft = models.resnext101_32x8d() # 设置 固定特征提取层 set_parameter_requires_grad(model_ft, feature_extract) # 调整分类个数 num_ftrs = model_ft.fc.in_features # 修改fc 的分类个数 model_ft.fc = nn.Sequential( nn.Dropout(0.2), # 防止过拟合 nn.Linear(in_features=num_ftrs, out_features=num_classes) ) else: print('Invalid model name,exiting..') exit() return model_ft import codecs def class_id2name(): ''' 标签关系映射 :return: ''' clz_id2name = {} for line in codecs.open('data/garbage_label.txt','r',encoding='utf-8'): line = line.strip() _id = line.split(":")[0] _name = line.split(":")[1] clz_id2name[int(_id)] = _name return clz_id2name